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TAC Credit WorkshopSelected slides
March 5, 2008
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Contents
1. Introduction and Results of Credit Practice Review
2. Credit Loss Model
3. Model Results
4. Wrap Up and Next Steps
5. Appendix
Introduction and Results of Credit Practice Review
Section 1
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The entire credit evaluation project covered three workblocks
Credit practices reviewCredit scoring
modelCredit loss
model
Developed a set of credit rating tools to assess probabilities of default (PD) for each participant
Identified model factors based on financial data and qualitative assessments
Tested againstavailable benchmarks
Included collateral limits, price caps, other key assumptions as inputs
Looked at possible volumetric exposures for each participant
Simulated market prices, which with the volumes yield exposure at default (EAD)
Simulated losses fromcredit failures
Explored the impact of exogenous variables/stress events
Assessed ERCOT’s current credit management practices
Assessed ERCOT’s current creditworthiness practices
Examined nodal impacts
Workblock 1 Workblock 2 Workblock 3
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Credit Practice Review – Summary ResultsERCOT’s credit worthiness monitoring & reporting and workout and management practices were found to be very solid. However, in the following areas ERCOT fell short of “best practices”:
CategoryPriority level
Calibration relative to best practice
Status achieved at the end of the project Initial practice
Progress achieved during this project Potential next steps
Risk appetite
High • Some internal discussion in market meetings
• Risk appetite definition should be explicitly defined to better guide ERCOT’s risk policies
• Estimate credit risk using credit loss model (current OW effort)
• Assess market’s comfort level with loss estimates and ability to absorb losses
• Board should develop a formal risk appetite statement
• Ensure credit policies and procedures are consistent with risk appetite and tolerance
Credit Scoring
Medium • Agency ratings used where available but primarily for limit setting purposes
• Creditworthiness was assessed using risk factors common to credit scoring models.
• Internal scoring model fully vetted and now available to supplement agency ratings
• Refine credit scoring model as additional data becomes available
7© 2008 Oliver Wyman www.oliverwyman.com
Credit Practice Review – Summary Results ERCOT’s credit worthiness monitoring & reporting and workout and management practices were found to be very solid. However, in the following areas ERCOT fell short of “best practices”:
CategoryPriority level
Calibration relative to best practice
Status achieved at the end of the project Initial practice
Progress achieved during this project Potential next steps
Exposure measure-ment and monitoring
High Exposure calculations track very recent historical exposure activity
Measurement of forward exposure is based on recent history
Processes are being automated
Response to alerts is rapid and well-defined
Credit loss model can simulate potential future exposure under a variety of assumptions and circumstances
Forward exposure measurement should be based on forward risk factors (e.g. forward price and volume estimates)
Loss reserve and capital
High Some single scenario estimates have been made
Based on historical market circumstances
Credit loss model provide best practice capability
Credit loss model will estimate loss magnitude
Use economic capital results to foster discussion regarding risk appetite and a more consistent framework for considering loss reserves
Credit Loss Model
Section 2
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Credit loss modelingThe questions this type of model addresses center on the potential forcredit-related losses
What level of credit losses is
“normal”?
What is the greatest loss we can expect?
How do changes to inputs affect potential losses?
Quarterly or annually
This loss amount will vary, and is considered the expected loss
Business must accommodate these
Over a given period
For a given levelof confidence
Under a given set of assumptions
Given a standardfor solvency, can be used for determining economic capital required
Price cap levels
Impact of credit and collateral rules
Through process changes; billing cycle, masstransition handling, market rules
Monitoring effort enhancements
Expected Loss Economic Capital
Approach
Model the inputs of interest in a way that captures the important characteristicsand relationships
Simulate the resulting market environment and the occasional default of the participants
Calculate the losses resulting from each simulation, and examine these statistics
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Credit Loss Model – High level credit loss calculation configurationThe model consists of four modules: default, price, volumetric exposure and collateral
Simulated prices per day per hub1
Price Module
Simulates daily prices per hub over the specified time horizon
List of defaulted QSEs by scenario
Generates correlated default scenarios over the specified time horizon
ExposurebyQSE
Volumetric Exposure Module
Calculates exposure for defaulted QSEs using simulated prices and volumes
Collateral Module
CollateralbyQSE
Calculates collateral for each of thedefaulting QSEs
Based on exposure and collateral of defaulting QSEs, calculates loss (if any) for each simulation and summarizes results across all simulations
Default Module
The model will be run thousands of times in order to estimate a credit loss distribution – this schematic represents one simulation
1
2 3
4
Aggregate losses across all QSEs
Loss Calculation
5
1. Hub refers to a zone, settlement point, location or market
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The model allows the user to make adjustments to inputs and measure how those changes impact the prospective distribution of credit losses
Price movement correlation between zones
Forward prices predicted from forward gas prices, based on local spark spreads
Frequency and size of jumps
Jump event types (1-, 3-, 6-day jump series)
Frequency of jumps common to multiple zones
Locational differences that drive CRR pricing
Price module inputs
Credit score of each QSE (i.e., probability of default)
Default correlation types
Market event sensitivity types
Number of days to post collateral and cure a breach
Simplified collateral calculations
Collateral haircuts
Settlement and billing cycle
Volume escalation behavior
Maximum potential volume
Length of time of mass transition (if applicable)
Default module inputs
Collateral module inputsExposure module inputs
Time horizon (in days)
Number of simulations
Global inputs
Number of hubs/zones
Number of QSEs
Model Results
Section 3
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0
500
1,000
1,500
2,000
2,500
3,000
3,500
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4
Nu
mb
er o
f si
mu
lati
on
s
Confidence levels in Monte Carlo analysis Results: Baseline case showing 8,500 of 10,000 simulations
Histogram shows number of simulations with credit losses less than, or equal, to X MM dollars
Zero, or rather small, losses are the most common result– Almost a third (3,134) of the simulations had no losses; either no defaults or defaults with adequate collateral– The results show that 80% of the simulations result in losses that are less than $2,200,000 each (the first 12
bars total 7,993 simulations)
The average loss across all simulations is about $3 MM – Most simulations are well below this, thus a few, rare, loss simulations have much greater losses– “Average” is not “most common outcome”, but the long run average across all outcomes (the Expected Loss)
These results are specific to one set of inputs, and one set of simulations
The pattern shown here is common to virtually every analysis of ERCOT’s market performed to date– All have a most common result of zero loss– All are heavily skewed to the right, showing only relatively rare, very large losses
Average
Losses ($MM)
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The baseline scenario reflects a combination of market and behavioral assumptions that are easily conceivable for the current market conditions and yields annual losses of
– $16 MM at the once-in-20-years level
– $43 MM at the once-in-100-years level
– $99 MM at the once-in-1,000-years level
The comparison stress scenario shown uses identical assumptions to the baseline except that all collateral actually held at the beginning of the period is recognized
– Baseline assumes that all collateral holdings will meet but not exceed ERCOT’s required minimums
50% of the annual credit losses were less than $194,000
Most larger loss simulationsare the result of several participants defaulting withinthe one year horizon
While these estimates represent reasonable estimations of potential losses, actual losses may be more or less than these, as all possible scenarios are not addressed
Tabular results and comparison for the same Baseline case
Baseline Comparison
Average Loss 2.95 .742
Median .194 .033
90.0th% 8.26 1.38
95.0th% 15.8 3.96
99.0th% 42.6 10.9
99.9th% 99.8 29.8
Maximum 213.0 156.0
Collateral held Min. per Protocols Actual historic
All losses in $ Millions
15© 2008 Oliver Wyman www.oliverwyman.com
Frame of reference - Confidence levels in corporate finance
This table shows historical default rates for firms with a variety of S&P credit ratings
The “1-yr PD” is the likelihood a firm with this rating will default for any reason within one year.
The “Confidence level” can be thought of as the likelihood that a firm with this rating will still be solvent after one year has passed, or the fraction of firms holding this rating that will remain solvent over the year
Some firms use a target rating as a solvency standard
– They manage their business so that the likelihood of bankruptcy within the next year equals the associated 1-yr PD
– For example, if they target BBB+, the probability of insolvency must be about 0.1%
– The amount of available assets the firm must hold to achieve this is its economic capital requirement
Rating 1-yr PD Conf level
AAA 0.002% 99.9980%
AA+ 0.003% 99.9970%
AA 0.005% 99.9950%
AA- 0.010% 99.9900%
A+ 0.018% 99.9820%
A 0.033% 99.9670%
A- 0.059% 99.9410%
BBB+ 0.108% 99.8920%
BBB 0.185% 99.8150%
BBB- 0.354% 99.6460%
BB+ 0.642% 99.3580%
BB 1.164% 98.8360%
BB- 2.111% 97.8890%
B+ 3.828% 96.1720%
B 6.943% 93.0570%
B- 12.59% 87.4080%
CCC+ 22.84% 77.1620%
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Choosing an appropriate confidence levelTypically driven by the needs of the various stakeholders
Stakeholders that are typically considered:
– Board, Management, Regulators, Debtholders, Shareholders,
– Financial community ,Customers, Suppliers, Employees
Selection of a confidence level typically hinges on these entities’ expectations of solvency, and what level of assurance is needed to retain them as stakeholders
Many firms with significant borrowing choose historical solvency levels associated with a target debt rating – as a way to drive towards particular bond ratings
The market participants invest in this region (plant, human capital, etc) with the expectation that the ERCOT market will remain functional
What expectation of solvency is appropriate for this market?
– A higher target will increase assurance, and current costs (collateral, etc) for the participants but demand more from them in explicit support
– A low target will decrease all of these
– The size and visibility of the market argue strongly for an investment grade target
Other strategic issues may also impact that choice, such as reputation, similarity to other ISOs, target growth in number of market participants or in a particular market segment
Wrap Up and Next Steps
Section 4
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Did ERCOT get everything it wanted?
Specific project objectives:– Review of credit practices in ERCOT
Protocols, Creditworthiness Standards, and credit risk management practices generally
– Determine whether ERCOT’s practices are consistent with best practices.
– Provide modeling capability to enable quantification of credit risks for the entire credit portfolio.
– Estimate Probabilities of Default (PDs) for each participant
– Estimate the credit loss probability distribution using this model
– Provide a capital adequacy assessment.
Deliverables:– Evaluation of creditworthiness and credit
management practices– Credit scoring model and documentation– Credit loss model with documentation – Loss distributions and capital adequacy
evaluation
All of the project’s objectives and deliverables have been achieved
1) At a specific point in time and for a specific timeframe, we are xx% confident that the market will not have losses in excess of $xx.
- The model OW delivered will allow ERCOT and the market to make this kind of statement under various assumption sets
2) At a specific point in time and for a specific time frame, we are xx% confident that the market can withstand losses of $xx.
- OW explored various ways to accomplish this with ERCOT.
- Ultimately, OW and ERCOT concluded that a model couldn't do this because ERCOT does not hold a central pool of capital to provide an economic buffer against credit losses (or any losses) and there is no way to know with certainty how each participant will respond to given levels of short pay or uplift.
- ERCOT agreed that providing “confidence”, if there was not a strong basis for the conclusions, would be counterproductive.
ERCOT also sought answers and insight into broader questions of risk tolerance
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Potential next steps
Examine any specific potential loss scenarios suggested by the Finance and Audit Committee and/or the Board
Continued education and iteration on scenarios with stakeholders
Pursue policy decision on level of acceptable credit exposure– Define an appropriate confidence level– Define a target “not to exceed” amount at the defined confidence level– Agree on the modeling assumptions to be used in the analysis
Appendix
Section 5
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Credit loss and capital adequacy definitions
Capital adequacy (economic capital): Based on the portfolio analysis and an assessment of the market, it is the amount of losses you may lose over a specified time period with probability X%
Expected Loss: Long run statistical average of potential credit losses across a range of typical economic conditions
Portfolio analysis: Aggregation of losses by counterparty across the market
Terms used when measuring credit loss Probability of default: The probability that a counterparty will default at some point in a
specified time horizon– Default correlation: Similarity of the counterparty to other counterparties in the portfolio in
terms of common drivers of default (e.g. geography, industry, business model)
Exposure at Default: Sum of the exposures at time of default for each counterparty over the specified time horizon
Loss given default: Sum of exposures in excess of collateral and other risk mitigation at time of default for each counterparty over the specified time horizon
Illustrative Loss Distribution
Pro
bab
ility
Unexpected LossesExpected Losses
Economic Capital
Expected Loss
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Near-term PD estimates for the capital adequacy model are approximated differently depending on the category a QSE falls under
Non-rated with financials
Non-rated without
financials
Publicly rated
Special case for un-rated
subsidiary with rated parent
Segment Proposed approach
Credit scoring model used to rate this segment Quantitative score calculated from provided financials Qualitative score started out the same for each QSE, but ERCOT adjusted for highly
positive or negative answers to qualitative questions
All QSEs in this segment receive a CCC+ rating Rating mapped to a PD
Public rating mapped to a PD
All QSEs in this segment receive a standalone CCC+ rating (if financials were not provided) or the rating from the credit scoring model
Parent receives their public rating Group logic applied to determine strength of relationship between subsidiary and
parent and QSE rating adjusted accordingly
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A standard credit scoring approach blends quantitative and qualitative scores and potential adjustments, to arrive at a PD and risk rating
Qualitative factors
Quantitative factors
Qualitative score
Quantitative score
AdjustmentsBlended
scoreRisk
rating
Probability of default
(PD)
For example:Total assetsEBITDA/assets
For example:Policies and proceduresManagement experience
For example:Warning signals
Ideally, a portfolio should be segmented so that entities within each group have similar risk characteristics– This may require different models or different weights
within one model
Segmentation of a portfolio can be performed along different dimensions (e.g., size, sector)
Segmentation
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The scoring approach groups output into a rating category with an associated midpoint PD so as not to overestimate precision
Internal credit
scoring model
Map PD to a rating category
Resulting PD
30bps
Final output based on rating and midpoint PD
BBB33bps
Example
1. All lower PDs map to this rating
PD range (bps) Rating
Midpoint PD (bps)
3-5 AAA-A+ 4
5-10 A+-A- 8
10-15 BBB+ 13
15-25 BBB 20
25-40 BBB- 33
40-80 BB+ 60
80-135 BB 108
135-220 BB- 178
220-365 B+ 293
365-600 B 483
600-1000 B- 800
> 10001 CCC+ 1500
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Selected financial and qualitative factors and weights
Quantitative factors Qualitative factors
Proposed factor Weight
Working Capital/Sales 30%
Current Ratio 10%
Equity/Assets 20%
EBITDA/Interest Expense 10%
EBITDA/Sales 10%
Net Income/Assets 10%
Total Assets 10%
Proposed factor Weight
Ability to access funding in difficult market environment 25%
Margin call and late payment history 20%
Experience of company leadership 15%
Recent growth 15%
Risk management policies and practices 10%
Quality and timeliness of reporting of financial information 10%
Length of time as QSE 5%
Quantitative score
Qualitative score
Blended score
Improve
No impact
Deteriorate
70% weighting 30% weighting
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Credit scoring results are used as input for credit loss modeling
Oliver Wyman used the model assumptions discussed on the previous pages to arrive at initial Probabilities of Default (PDs) for each QSE
– Some of these were agency ratings
– Some were scored based on financials provided to ERCOT
– Others were assigned CCC+ when no financials were provided
All of these initial ratings were considered in light of any relationship between the participant and a parent (i.e., “Group Logic” was applied)
Credit loss model treats capped guarantees with 30-day termination clauses as collateral
– Where the guarantee is substantially in excess of EAL, should net same results
– Best allows for all possible scenarios where and how entities use guarantees
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Default correlationDefaults between QSEs are correlated by common drivers
Probabilities of default are user inputs, intended to feed directly from the internal credit scoring model
Each QSE is associated with a “default correlation” type
– These types are based on common drivers of default
– These common drivers systematically increase the probability of QSEs within the same type (and across types) defaulting together
– Selection of “default correlation” types should attempt to best segment the QSEs by common default drivers
The proposed “default correlation” types are based on the primary business of each QSE as defined below
Default correlation type Business Definition
1 Generation > 70% of combined load and generation volume is generation1
2 Small load < 10,000 MWh/day of load (and < 30% of combined load and generation volume is generation) 1
3 Large load > 10,000 MWh/day of load (and < 30% of combined load and generation volume is generation) 1
4 Trading Minimal load or generation
5 Public power Munis and coops
6 Mixed Relatively balanced mix of load and generation
1 Based on average activity for a recent month.
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Default events are correlated based on business type
Each individual QSE is assigned a “default correlation” type based on their business
The correlations determine the likelihood that QSEs will default within the same timeframe, driven by the same underlying factors
In other industries, default correlation within industry segments is 20-30%
The correlations proposed are subjective, based on the business risk factors present in these enterprises
Default type
Generation Small load Large load Trading Public power Mixed
1 2 3 4 5 6
1 Generation 20%
2 Small load 0% 30%
3 Large load 0% 20% 25%
4 Trading 0% 0% 0% 10%
5 Public power 10% 5% 10% 0% 20%
6 Mixed 10% 5% 5% 5% 10% 20%
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Defaults can either be market driven or non-market driven“Market event sensitivity” types are used to determine how a QSE may have defaulted
“Market event sensitivity” types are identified based on the likelihood of QSE defaults being closely associated with market events (e.g., price jumps)
– If certain QSEs are more likely to have defaults near market events (high price days), the model needs to reflect this in order to accurately calculate exposure
If the QSE’s default is identified as being related to a market event, the prices near the default day are above a specified percentile
If the QSE’s default is identified as having no relation to a market event, the day of default will be randomly chosen over the time horizon of the analysis
Depending on a counterparty’s market event sensitivity and type, volume escalation scenarios will be linked accordingly
Type DescriptionProbability of defaulting near a “high price day”
“High price day” is defined as those in the upper
1 SR / LR
50% 90%
2 Gen, Trader, PP, Mixed 20% 90%
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Price jump analysis
Identify jump cutoff levels
Attempt to leave jumps and residual price changes “normal”
Assumptions include– One common cutoff level vs individual cutoffs– Identical size jumps for concurrent events– Simple average daily prices vs weighted averages
$0
$50
$100
$150
$200
$250
Sep-05 Nov-05 Jan-06 Mar-06 May-06 Jul-06 Sep-06 Nov-06 Jan-07 Mar-07 May-07 Jul-07 Sep-07
Date
Pri
ce
North South West Houston
Jump cutoff 105 103 107 98
Observed price days 760 760 760 760
Observed jump days 34 33 28 47
Avg jump size (above mean) 76.1 68.9 78.3 69.5
St dev jump size 27.8 23.0 27.2 27.0
Skew1 0.922 0.937 0.930 0.887
Kurtosis2 -0.091 -0.346 -0.033 -0.648
J-B test for normality 4.687 4.357 3.998 4.892
Normal? Normal Normal Normal Normal
Jump frequency 4.5% 4.3% 3.7% 6.2%
Illustrative
1 Skew characterizes the degree of asymmetry of a distribution around its mean.2 Kurtosis characterizes the relative peakedness or flatness of a distribution compared with the normal distribution.
31© 2008 Oliver Wyman www.oliverwyman.com
Proposed market price characteristics and choicesPrice parameters were directly calculated from or informed by historical ERCOT price data and can be set distinctly for each hub
Category Historical ranges Price assumptions
Frequency of jump days 4.6-5.6% 7-10%
Percent likelihood of a 1-, 3- , or 6-day jump series 79%, 17%, 4% respectively 75%, 20%, 5% respectively
Frequency of jumps common to multiple zones 80% 80%
Average jump size (above base price) 64-69 $/MWh (1.2 hr / day) ~ 80 $/MWh
99th % highest expected jump (reflects price cap in desired market design)
123-147 $/MWh (2.25 hr / day) ~ 375 $/MWh
Correlation of normal daily price movements among locations
Jump parameters
North South West Houston
North 100% 87% 92% 91%
South 87% 100% 86% 90%
West 92% 86% 100% 86%
Houston 91% 90% 86% 100%
Prices for nodal can be simulated using adjusted parameters
Correlation between RT and DAM expected to be very high (95% proposed)
May include smaller, less frequent jumps
New correlation matrix
New jump parameters for DAM
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Exposure ModuleKey modeling assumptions or issues
Default mode drives exposure period
Relationship of default to
market events
Volume escalation potential
Based on whether the default event was market-driven or not, certain volume escalation scenarios will follow to reflect the potential for increasing participation in the BES market
The user can specify likelihoods of escalation levels, where escalation is based on a percent movement between historical averages and maximum volume
Market events
– The model will use prices in the counterparty’s primary hub (hub with the most volume)
– The default is placed near a price jump event (1-,3-,6-day jump events exist)
– The jump event chosen will be the longest in the price series (e.g., the model will first look for a 6-day series, but if not present the model will look for a 3-day series, etc.)
Non-market events
– The default is placed randomly within the time horizon of the analysis
The number of days over which volumetric exposure to BES prices occur is driven by the default mode
Two modes are currently considered; mass transition and bankruptcy/leaving the market
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Our approach to volumetric exposure allows for a range of possible scenarios
IllustrativeSimple example 1 – Volumetric exposure during a market event for a load-serving QSE
Market trigger eventBefore the market trigger event
After the market trigger event
Volume may escalate to 20% toward the maximum with 60% probability
2
Volume may remain at escalated levels
AVolume may escalate to 100% of the maximum with 40% probability
1
Volume at historical levels
Volume may return to historical levels
B
Simple example 2 – Volumetric exposure during a non-market event for a load-serving QSE
Before the non-market trigger event After the non-market trigger event
Volume may escalate to 100% of the maximum with 80% probability
1
Volume at historical levelsVolume may escalate to 50% of the maximum with 20% probability
2
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Volume escalation assumptions
During a market eventRed to 0 Main Hist 20% 40% 70% 100%
Generators 10% 50% 30% 9% 0% 1%
Small retailer 5% 20% 40% 10% 0% 25%
All others 0% 50% 40% 9% 0% 1%
Note: Escalations for non-market events are similar
After a market event
Maintain at Return to
escalation historical levels Maximum
Gen/LR/PP/Mixed 30% 70%
Small retailer 30% 70%
Traders 0% 100%
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Collateral ModuleKey modeling assumptions or issues
Haircuts for collateral types
Haircuts may be applied to different collateral types (e.g. letter of credit vs. cash)
Simplified calculation to identify key drivers
The calculation focuses on exposure due to price and volume Based on activity in BES, RT and DAM Excludes additional adjustments (e.g., PU, TCRs) which are not easily predictable,
nor the key drivers of loss
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Conceptual model of collateral adjustmentStructured to reflect ERCOT protocols
The collateral module is designed to simulate ERCOT’s collateral calculations for the current and nodal market
Collateral requirements will be simulated for BES, RT and DAM activity
Impacts of other billing determinants are not considered
Collateral is required based on the higher of EAL and NLRI (or AIL for nodal)
EAL – Estimated Aggregate Liability
Average daily transaction (ADT) calculated based on latest two invoices
ADT extrapolated to 40 days (ADTE)
EAL is the highest ADTE during previous 60-day period (~9 weeks)
Estimated activity for outstanding invoices (OUT) for BES, RT and DAM will be included in the calculation
Additional adjustments are applied, but will not be included in the model– TCR auction revenue– Potential uplift– Other miscellaneous invoices
NLRI – Net Load/Resource Imbalance Liability
Accounts for invoice periods that are completed but not invoiced and invoice periods not yet completed
For twenty-one uninvoiced days:– Price * estimated volume
For seven forward-projected days:– (Price * 150%) * yesterday’s volume
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Key Stress Tests – Zonal market design Many variations in inputs and assumptions have been examined
Primary stress tests focused on market (price) and participant (escalation and sensitivity) behaviors
Withdrawal of excess collateral (above ERCOT requirements) prior to default– This assumption directly increased net losses– Primarily for larger participants, whose defaults tend to drive the tails of the loss distribution– Greatly accentuates the impact of all other stress factors
Ability and likelihood of defaulting participants increasing their exposure to the market toward (or to) their maximal potential (volume escalation)– Losses are very sensitive to this parameter choice, since the largest counterparties are orders of
magnitude bigger than the smaller counterparties– Collateral is based on recent invoicing, thus recent activity rather than potential activity
Higher prices and/or more, higher and longer duration price spikes– Alone, this stress test produced only slightly higher losses– In conjunction with enhanced escalation, impact increased noticeably
Correlation of defaults with price spikes (aka, market event sensitivity)– Increasing this correlation increased losses in the loss distribution tails, but not in the extreme tails– Extreme tail losses were likely already caused by default on high price days
Credit quality or rating of the participants– Increasing credit quality decreases the number of defaults in any single simulation– Also shifts the loss distribution down as there are more cases with no defaults– Loss given default is unchanged, although the multiple defaulting entity cases are diminished
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Key Stress Tests – Nodal market design Additional situations should be studied when data become available
Nodal market design version of the credit loss model differs somewhat from the Zonal market version
– Both RT and DAM markets can be represented
– Price modeling at RT and DAM locations is identical to the Zonal BES market model (mean reversion, jumps, correlations, etc)
– The spirit of the current market rules for collateral have been reflected in the model logic
– CRR holdings can be accommodated, with valuations for the realized and unrealized portions
The reasonableness of the overall credit loss results from this model are currently difficult to assess, because there is no firm basis for many of the required assumptions
– Volume of participation by each counterparty in each DAM and each RT market
– Price behavior at the DAM and RT locations
– Number of DAM and RT locations to consider
– Number, tenor, size and location of the CRRs held by each counterparty
– Collateral is based on recent invoicing, thus recent activity rather than potential activity
As data is collected, some of these parameters can be estimated
Initial model runs can test some of the remaining assumptions, by varying those parameters
Credit scoring and the estimation of counterparty PDs will be unchanged
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